1. Field of the Invention
This disclosure relates generally to the field of self-optimizing methods and machines, and in particular to a multi-variable, real-time self-optimizing method and machine, for operation in practical or non-ideal conditions and used to automatically develop computer software.
2. Description of Related Art
Computer-controlled automation systems are widely used. These systems are extremely powerful. With their uses, consistency is achieved together with the usually, but not necessarily always, associated improved profits, productivity, and product or service qualities (P.sup.3 Q).
Further, by transferring to machines human intelligence, rather than skill, these systems have ushered us into a Second Industrial Revolution.
But human intelligence or knowledge bases only document averaged previous results on old samples with design, equipment, materials, parts, procedures, or environment different from the present or future results. Inherent are errors due to the various samplings, assumptions, or extrapolations, and repeated human interactions. These knowledge bases are often incomplete, inaccurate, biased, erroneous, out-of-date, and/or too generalized for uses in a particular automation task with a specific combination of design, equipment, procedures, materials, parts, and environment.
Detailed knowledge bases on modern technologies are particularly lacking. Even many old technologies are not fully mastered or understood. Each of these technologies involves many processing steps often with unknown chemical, mechanical, electromagnetic, aerodynamic, fluidic, or other phenomena on the subatomic, atomic, microscopic, macroscopic, or other levels. Even the controlling variables in each phenomenon are often not completely known, and certainly have not been comprehensively studied. In fact, much research and development (R&D) remains to be done in every field. Yet time is very short in this highly competitive world. A new R&D method must, therefore, be developed.
For example, system dynamics of modern processes and equipment are generally ill-defined. Chemical and metallurgical reactions are often uncertain. The mechanism of catalysis is not completely known. Crystal growth is still an art. After millions of controlled welding experiments, the critical variables in the all-important welding process cannot yet be even identified among the very many possible variables. In the case of the new high temperature ceramic superconductors, the samples are still somewhat hard to make, shape, purify, reproduce, isolate, stabilize, confirm, or even determine compositions.
Without reliable knowledge bases, the usual automation specialists would be at a loss in selecting the few among many manufacturing or servicing phenomena or variables to be controlled, as well as in formulating the system dynamics models, in setting up the control equations, in determining the control constants, and in specifying setpoints for the control variables.
The fragile and unreliable knowledge bases often give only partial or invalid system dynamics models, oversimplified control equations, and inexact or misleading control constants and setpoints. In addition, all the setpoints are too arbitrary and round-numbered (e.g., 800.degree. C. and not 796.768.degree. C., 3 feet per minute, 20 gallons) to be possibly optimal statistically. The chance of these variables or setpoints being optimal at any time, not to say instantaneously or continuously, is close to zero. The optimal setpoints cannot be constant, and are assumed in prior art automation systems, unless one assumes no effect due to changes or variations in time, equipment, procedures, materials, parts, and environment.
These conventional automation systems are also not smart and must be spoon-fed at every step via computer programs or master-slave instructions. They are not totally integrated or automated, and require constant human guidance, reviews, analyses, interactions, and supervision.
Due to this repeated human involvement, the conventional automation systems not only are greatly slowed down, but inevitably inherit the many defects of the inconsistent and imperfect human programmers, test planners, samplers, testers, data collectors and analyzers, communicators, and technicians. Humans are million times slower and less reliable than microprocessors in, e.g., memory recalling or storing, information inputting or outputting, data analyzing, communicating, and commanding or actuating.
In addition, usually these prior art systems merely passively adapt, adjust, correct, control, or regulate, in response to variations in the environment or a few control variables. Dealing with more than several interacting variables results in an extremely large number of tests to be made; and in a massive amount of data to be collected, conditioned, stored, and quickly or instantly analyzed. This is often impractical or impossible, because of the well-known problems of "combinatorial explosion" and "computer intractability," as has been described in the U.S. Pat. No. 4,710,864.
Yet, modern technologies invariably involve many unpredictable, interacting, and rapidly changing control variables in such categories as: design, material vendors, batches, lots, and conditions; compositioning; processing equipment; procedures in each of the many steps; and environment. Many phenomena are transient but highly non-reproducible yet are critical and need to be known and/or understood.
Process integration of optimization remains key to many modern processes. Process interaction and an extremely competitive market and environment make, e.g., semiconductor manufacture one of today's most daunting technological challenges. The profitable manufacture of advanced microelectronic circuits requires knitting together hundreds of process steps needed to make a single wafer. Each process step must be optimized not only to accommodate preceding and subsequent processes, but also to account for minute influences specific to the wafer itself. It is a complicated effect, and made even more so by the presence of the edge of the wafer and other seemingly trivial effects due to circuit pattern density, wafer position effect, and procedures and materials used in previous or subsequent processing steps. Many previously neglected variables on designs, materials, procedures, equipment, and environment must always be systematically optimized and controlled.
Artificial intelligence (AI) technologies, particularly expert systems and the neural networks, have been developed and increasingly used in various fields. But again the knowledge bases are often inadequate or deficient, particularly on developing technologies. The prior art expert systems are also costly, inflexible, qualitative, and often inaccurate and out-of-date particularly for complicated yet rapidly improving modern technologies. In addition, they too cannot handle the inherently large number of interacting variables.
Reliable and relevant knowledge is scarce and very costly. Up to now, the main bottleneck in the development of expert systems has been the acquiring of the knowledge in computer-usable form. Human knowledge often not only is fragile, costly, unreliable, but also difficult to be translated for use by machines. Codifying an expert's skill has always been a long and labor-intensive process.
Hence, experts conclude that machine learning is the key to the future of automation in general and to expert systems in particular. The valuable knowledge must be manufactured instantly, in bulk, and at low cost. So far, however, no such machines exist.
Conventional AI development environments experience difficulties in producing efficient real-time systems. This is due to the fact that the same code necessary to enhance the development environment tends to slow down the system during run-time. To overcome these limitations, AI system designers must embed a knowledge base (KB) into their own custom run-time AI shells to achieve real-time performance. Unfortunately, the deeper the KB is embedded into the actual code for improved performance, the harder it is to change the KB when maintenance is necessary. Therefore, the AI system designer must constantly balance system performance versus ease of maintaining and manipulating the KB. An automation system with real-time KB generating capacity would thus be highly desirable.
Prior art automation systems also invariably contain various hidden errors of samplings, assumptions, extrapolations, scaling-ups, and statistical fluctuations of uncertain magnitudes. These systems are also at the mercy of other errors due to, e.g., miscalibrated sensors, imperfect actuators, drift or instability in equipment, and partially damaged components. Any one of these errors can easily lead to unexpected inconsistencies in, e.g., manufacturing or servicing results.
Li has various U.S. patents on self-optimizing method and machine, for example, U.S. Pat. Nos. 4,368,509, 4,472,770, 4,710,864, 4,910,660, 5,079,690, and 5,410,634, each of which is incorporated herein by reference. These patents describe various self-optimizing methods and machines. Still, much remains to be done on automation in general-and on self-optimization in particular.
Accordingly, an object of the present invention is to provide improved a self-optimizing method and machine.
A further object of the invention is to provide a real-time self-optimizing method and machine capable of handling tens, hundreds, thousands, or more variables with minimum human guidance.
Another object of this invention is to provide a closed-loop, self-optimizing method and machine which can optimize practically continuously and instantly.
A broad object of the invention is to provide a self-optimizing method and machine which can self-plan controlled tests to be performed on the very particular method and machine itself without relying on many assumptions, scaling-up laws, and extrapolations from sampled test results obtained on other similar methods and machines; with the test data instantly analyzed for timely optimization results.
Another object of the invention is to provide a self-optimize method and machine in practical or non-ideal conditions by tolerating, neutralizing, or suppressing the effect of errors due to defective knowledge bases, miscalibrated sensors, imperfect actuators, drifting equipment, damaged components, and statistical fluctuations.
A further object of the invention is to provide a self-optimizing method and machine which operates with deficient and minimal or practically zero knowledge bases, which rapidly generates its own new knowledge bases through automatic R&D, and which immediately and continuously replaces these new knowledge bases with still newer and more accurate knowledge bases for continuously optimal results.
An additional object of the invention is to provide a self-optimizing method and machine which actively computes and automatically sets the instantaneous optimal combinations of the many relevant variables in various categories, with instant feed-back to supply data for immediate replanning, retesting, and re-optimizing, all without human intervention.
Another object of the invention is to manufacture, in bulk and at low cost, reliable knowledge bases for the instant computer-coding and development of relevant expert systems.
Yet another object of the invention is to provide a self-optimized computer system software and service for businesses, offices, education, training, engineering, reverse-engineering, designing, processing, manufacturing, distribution, R&D, communication, reconnaissance, surveillance, project management, data analyses, decision making, running supercomputers or parallel computers, multimedia computing or networking, electronic data interchanging, computer operations and applications, and other applications.
A further object of the invention is to provide computer-generated software or entities, such as software objects, which, all by themselves and with blinding speed, reproduce or multiply, mutate or undergo spontaneous genetic changes in the best possible way under a given environment.
Further objects and advantages of my invention will appear as the specification is described herein.